Project Reference Number: ARCP2013-01CMY-Patra/Canadell

Greenhouse gas budgets of South and Southeast Asia

The following collaborator worked on this project: 1. Prabir K. Patra (Project Leader), JAMSTEC, [email protected]

2. Josep G. Canadell (Project Leader), CSIRO, [email protected]

Jo

Project Reference Number: ARCP2013-01CMY-Patra/Canadell

Greenhouse gas budgets of South and Southeast Asia

Final Report Submitted to APN

©Asia-Pacific Network for Global Change Research

Part One: Overview of Project Work and Outcomes

Non-Technical Summary Within the United Nations Framework Convention on Climate Change, countries are continuing to negotiate emission reduction targets and exploring mitigation strategies best suited to their technical capabilities and regional biophysical characteristics. One of the largest impediments to advance on the latter is the lack of high quality estimates of greenhouse gas (GHG) fluxes in and out of natural and managed ecosystems. In this project, we have undertaken the most ambitious synthesis effort to date using global and regional datasets and model outputs to constrain the regional GHG budgets of South and Southeast Asia, where the source/sink balance of GHGs has large uncertainties.

Keywords Greenhouse Gases, Regional Sources and Sinks, South and Southeast Asia

Objectives The main goal of this proposal is to compile and synthesize information from multiple sources, essential to understand the role of climate-human systems in the production of greenhouse gas emissions. This will be achieved through cooperation between scientists from South and Southeast Asia, and experts from other parts of the world. Mean estimates of the GHGs balance at the regional level and attribution to flux components will be valuable information for climate policy development in the region, particularly of mitigation policies. Several workshops and personnel visits among the participating institutions are planned for improving exchange of observational data, training on numerical model, and analysis tools.

Amount Received and Number of Years Supported The Grant awarded to this project was: US$ 50,000 for Year 1: US$ 50,000 for Year 2: US$ 45,000 for Year 3:

Activity Undertaken  

The South Asian Carbon Budget The Southeast Asian Carbon Budget

Results With this third year of reporting, we have now completed the three mains tasks we set up to do:

Final Report: ARCP2013-01CMY-Patra/Canadell

1

1. To estimate the source and sinks of carbon dioxide (CO2) and methane (CH4) due to anthropogenic and natural biospheric activities for the South Asia region (Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka). Flux estimates were based on top-down methods that use inversions of atmospheric data, and bottom-up methods that use field observations, satellite data, and terrestrial ecosystem models. Based on atmospheric CO2 inversions, the net biospheric CO2 flux in South Asia (equivalent to the Net Biome Productivity, NBP) was a sink, estimated at -104±150 TgC yr-1 during 2007-2008. Based on the bottom-up approach, the net biospheric CO2 flux is estimated to be -191±193 TgC yr-1 during the period of 2000-2009. This last net flux results from the following flux components: (1) the Net Ecosystem Productivity, NEP (net primary production minus heterotrophic respiration) of 220±186 TgC yr-1 (2) the annual net carbon flux from land-use change of -14±50 TgC yr-1, which resulted from a sink of -16 TgC yr-1 due to the establishment of tree plantations and wood harvest, and a source of 2 TgC yr-1 due to the expansion of croplands; (3) the riverine export flux from terrestrial ecosystems to the coastal oceans of +42.9 TgC yr-1; and (4) the net CO2 emission due to biomass burning of +44.1±13.7 TgC yr-1. Including the emissions from the combustion of fossil fuels of 444 TgC yr-1 for the decades of 2000s, we estimate a net CO2 land-to-atmosphere flux of 297 TgC yr-1. In addition to CO2, a fraction of the sequestered carbon in terrestrial ecosystems is released to the atmosphere as CH4. Based on bottom-up and top-down estimates, and chemistry-transport modelling, we estimate that 37±3.7 TgC-CH4 yr-1 were released to atmosphere from South Asia during the 2000s. Taking all CO2 and CH4 fluxes together, our best estimate of the net land-to-atmosphere CO2-equivalent flux is a net source of 334 TgC yr-1 for the South Asia region during the 2000s. If CH4 emissions are weighted by radiative forcing of molecular CH4, the total CO2-equivalent flux increases to 1148 TgC yr-1 suggesting there is great potential of reducing CH4 emissions for stabilizing greenhouse gases concentrations (Patra et al., 2013) 2. To estimate the source and sinks of carbon dioxide and methane due to anthropogenic and natural biospheric activities for the Southeast Asia region (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Thailand, TimorLeste, Papua New Guinea, Singapore, and Vietnam). The bottom-up biospheric carbon budget is +209 TgC yr-1, and the total carbon budget including fossil fuel emissions is +463 TgC yr-1. The top-down biospheric carbon budget is +365 TgC yr-1. These results are will be detailed in an upcoming peer-reviewed publication (Canadell et al., in prep.). 3. To support the analysis of air-sea CO2 exchange over the Indian Ocean, which is connecting the South and Southeast Asian region. The Indian Ocean (44oS–30oN) plays an important role in the global carbon cycle, yet it remains one of the most poorly sampled ocean regions. Several approaches have been used to estimate net sea–air CO2 fluxes in this region: interpolated observations, ocean biogeochemical models, atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Indian Ocean sea–air CO2 fluxes between 1990 and 2009. Using all of the models and inversions, the median annual mean sea–air CO2 uptake of −0.37±0.06 PgC yr-1 is consistent with the −0.24±0.12 PgC yr-1 calculated from observations. The fluxes from the 2

Final Report: ARCP2013-01CMY-Patra/Canadell

southern Indian Ocean (18–44oS; −0.43±0.07 PgC yr-1) are similar in magnitude to the annual uptake for the entire Indian Ocean. The results of these three activities and products were used in the 5th assessment report of the Intergovernmental Panel on Climate Change (IPCC) for chapter 6 of Working Group 1, i.e., Carbon and Other Biogeochemical Cycles (Ciais et al., 2013). Dr. Canadell is a lead author and Dr. Patra is a contributory author to this chapter of the IPCC AR5. We have also begun air sampling from Comilla, Bangladesh (23.45oN, 91.20oE), as a collaborative effort between JAMSTEC and National Institute for Environmental Studies (NIES) in Japan, and Dhaka University and Bangladesh Meteorological Department (BMD) in Bangladesh. This activity records concentrations of all major greenhouse gases (CO2, CH4, N2O, CO, H2, SF6) since June 2012 at weekly time intervals. Three international workshops on Asian greenhouse gases budget have been conducted to discuss and share information on the state of knowledge of various source sectors of CO2, CH4 and N2O. These workshops have led to various peer reviewed research papers already published or shortly to be submitted. One training programme in Dhaka University for the master course students and young faculties was conducted. In each of these events about 50 people participated (details below).

Relevance to the APN Goals, Science Agenda and to Policy Processes The main goal of this proposal is to compile and synthesize information from multiple sources, essential to understand the role of climate-human systems in the production of greenhouse gas emissions. This will be achieved through cooperation between scientists from South and Southeast Asia, and experts from other parts of the world. Mean estimates of the GHGs balance at the regional level and attribution to flux components will be valuable information for climate policy development in the region, particularly of mitigation policies. Several workshops and personnel visits among the participating institutions have been conducted for improving exchange of observational data, training on numerical model, and analysis tools.

Self-evaluation Significant progress was made over the past one and half years towards the completion of the territorial budgets of CO2, CH4 , N2O for South and Southeast Asia. The full carbon budgets for the two regions are now available. Most importantly, the project has been able to create an international network of collaborators who are now working together beyond the APN grant, and so leaving a long-term legacy of APN’s investment in supporting the establishment of robust and comprehensive GHG budgets.

Potential for further work A whole Asian greenhouse gases budget will be the obvious next step, where the East, South and Southeast Asia regions can be combined in an analysis for all the major gases, e.g., CO2, CH4 , N2O, carbon monoxide (CO), and Black Carbon (BC).

Publications Canadell, J. G., et al., The Southeast Asian Carbon Budget, in prep. Final Report: ARCP2013-01CMY-Patra/Canadell

3

Patra, P. K., J. G. Canadell, and S. Lal, The rapidly changing greenhouse gas budget of Asia, EOS Trans. (AGU), 93, 237, doi: 10.1029/2012EO250006, June 2012. Patra, P. K., A. Ito, and X. Yan, Climate change and agriculture in Asia: A case study for methane emission due to rice cultivation, Studium press (India) Pvt. Ltd., October 2012. Patra, P. K., J. G. Canadell, R. A. Houghton, S. L. Piao, N.-H. Oh, P. Ciais, K. R. Manjunath, A. Chhabra, T. Wang, T. Bhattacharya, P. Bousquet, J. Hartman, A. Ito, E. Mayorga, Y. Niwa, P. A. Raymond, V. V. S. S. Sarma and R. Lasco, The carbon budget of South Asia, Biogeosciences, 10, 513-527, 2013. Sarma, V.V.S.S., A. Lenton, R. Law, N. Metzl, P. K. Patra, S. Doney, L.D. Lima, E. Dlugokencky, M. Ramonet, and V. Valsala, Sea-air CO2 fluxes in the Indian Ocean between 1990 and 2009, Biogeosciences, 10, 7035-7052, 2013.

References Ciais, P., C. Sabine, et al., Working group I, Contribution to the IPCC fifth assessment report (AR5), CLIMATE CHANGE 2013: THE PHYSICAL SCIENCE BASIS, Chapter 6: Carbon and Other Biogeochemical Cycles, Cambridge University Press, 2013.

Acknowledgments This work is a contribution to the REgional Carbon Cycle Assessment and Processes (RECCAP), an activity of the Global Carbon Project. The work is partly supported by JSPS/MEXT (Japan) KAKENHI-A (grant#22241008) and Asia Pacific Network (grant#ARCP2011-11NMY-Patra/Canadell). Canadell is supported by the Australian Climate Change Science Program of CSIRO-BOM-DCCEE. The inverse model results of atmospheric CO2 and terrestrial ecosystem model results are provided under TransCom (http://transcom.lsce.ipsl.fr) and TENDY (http://www-lscedods.cea.fr/invsat/RECCAP) projects, respectively, and we appreciate all the modelers’ contribution by providing access to their databases. We thank all our collaborators for this project, especially, Lingxi Zhou/China ([email protected]); Shilong Piao/China ([email protected]); Al Hooijer/Singapore ([email protected]); Rizaldi Boer/Indonesia ([email protected]); H. Simbolon/Indonesia (herbolon @indo.net.id); Manish Naja/India ([email protected]); Tapas Bhattacharya/India ([email protected]); Shuji Aoki/ Japan ([email protected]); Akihiko Ito/Japan ([email protected]); Toshinonu Machida/Japan ([email protected]); Yoshiki Yamagata/Japan ([email protected]); Guido van der Werf/The Netherlands ([email protected]); Richard A. Houghton/USA ([email protected]); Steven Sitch/UK ([email protected]); Kawser Ahmed/Bangladesh (kawser @univdhaka.edu); Monzul Hazarika/Thailand ([email protected]); Peter Rayner/Australia ([email protected]); Yosuke Niwa/Japan ([email protected]), Kentaro Ishijima/Japan ([email protected]), Chun-Ho Cho/Korea ([email protected])

4

Final Report: ARCP2013-01CMY-Patra/Canadell

Part Two: Technical Report

Preface The timing of this final report comes at a critical period of international negotiations in the United Nations Convention Framework on Climate Change. During this year nations will be pledging their voluntary mitigation commitments to the Intended Nationally Determined Contribution (INDC) database. This report and the associated publications bring the most up to date information on sources and sinks, trends and variability of greenhouse gases for nations in South Asia and Southeast Asia. This information is of strategic value to countries to determine those contributions including the possible role of land-based options as part of their mitigation portfolios.

Table of Contents 1.

Introduction ...................................................................................................................... 5

2.

Methodology .................................................................................................................... 6

3.

Results & Discussion ..................................................................................................... 10

4.

Conclusions ................................................................................................................... 26

5.

Future Directions ........................................................................................................... 27

References ............................................................................................................................ 31 Appendix ............................................................................................................................... 38

1. Introduction South Asia (Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka) is home to 1.6 billion people and covers an area of 4.5×106 km2. These countries are largely self-sufficient in food production through wide range of natural resources, and agricultural and farming practices (FRA, 2010). However, due to rapid economic growth, fossil fuel emissions have increased from 213 TgC yr-1 in 1990 to about 573 TgC yr-1 in 2009 (Boden et al., 2011). A detailed budget of CO2 exchange between the earth’s surface and the atmosphere is not available for the South Asia region due to a sparse network of key carbon observations such as atmospheric CO2, soil carbon stocks, woody biomass, and CO2 uptake and release by managed and unmanaged ecosystems. Only recently, Patra et al. (2011a) estimated net CO2 fluxes at seasonal time intervals by inverse modeling (also known as top-down approach), revealing strong carbon uptake of 149 TgC month-1 during July-September following the summer monsoon rainfall.

Final Report: ARCP2013-01CMY-Patra/Canadell

5

Southeast Asia (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Thailand, Timor-Leste, Papua New Guinea, Singapore, and Vietnam) is a complex geographical and geopolitical region. It has mainland and peninsular components dominated by tropical rainforest and cropland, and savanna woodlands in drier areas of the northeast. Forest cover constitutes 214 million hectares equal to 29 percent of the Asia-Pacific region’s total forest area (FAO 2011). Most of the region is considered a biodiversity hotspot because it harbours a high number of endemic species largely threatened by forest loss (Myers et al. 2000; Sodhi et al. 2010). The region is also home to the most extensive tropical peatlands in the world with an estimated carbon pool of 68 PgC (Page et al. 2011), a quantity rapidly diminishing along with the loss of the remaining swamp forest due to deforestation and drainage (Hooijer et al. 2010; Miettinen et al. 2011). The regions are also very likely to be a strong source of CH4 due to rice cultivation by an amount, which still remains controversial in the literature (Cicerone and Shetter, 1981; Fung et al., 1991; Yan et al., 2009), and large numbers of ruminants linked to religious and farming practices (Yamaji et al., 2003). Since the green revolution there has been an increase in CH4 emissions owing to the introduction of high-yielding crop species, increased use of nitrogen and phosphorus fertilizers, and expansion of cropland areas to meet the food demands of a growing human population in countries of South Asia (Bouwman et al., 2002; Patra et al., 2012a). South Asia has also undergone significant changes in the rates of land use change over the last 20 years contributing to the net carbon exchange. India alone has increased the extent of forest plantations by 4.5 Mha (~7% of 64 Mha) from 1990 to 2010 leading to a 26% increase in the carbon stock in living forest biomass (FRA 2010). Over the last twenty years, Southeast Asia has undergone through some of the largest changes in land use due to (1) government policies to open up forest areas, particularly in Indonesia for the transmigration program during 1990s, and (2) the rapid expansion of oil-palm plantations for the domestic and international food and biodiesel markets, along with afforestation of fast growing Acacia plantations for the pulp and paper markets (Murdiyarso et al. 2011). In this project we establish for the first time the net carbon budget of South and Southeast Asia, including CO2 and CH4 , and its inter-annual variability for the period 1990-2009. We achieve this goal by synthesizing the results of multiple approaches that include (1) atmospheric inversions as so-called top-down methods, and (2) fossil fuel consumption, forest/soil inventories, riverine exports, remote sensing products and dynamic global vegetation models as bottom-up methods. The comparison of independent and partially independent estimates from these various methods help to define the uncertainty in our knowledge on the South and Southeast Asian carbon budget. Finally, we attempt to separate the net carbon balance into its main contributing fluxes including fluxes from net primary production, heterotrophic respiration, land use change, fire, and riverine export to coastal oceans. This effort is consistent with and a contribution to the REgional Carbon Cycle Assessment and Processes (Canadell et al., 2011; Patra et al., 2012b).

2. Methodology The South Asia region designated for this study is shown in Figure 1, along with the basic ecosystem types (DeFries and Townshend, 1994). A large fraction of the area is cultivated 6

Final Report: ARCP2013-01CMY-Patra/Canadell

croplands and grassland or wooded grassland (1.3×106 km2 and 1.5×106 km2 or 0.89×106 km2, respectively). The rest of the area is classified as bare soil, shrubs, broadleaf evergreen, broadleaf deciduous and mixed coniferous (0.35×106 km2, 0.22×106 km2, 0.11×106 km2, 0.10×106 km2 and 0.05×106 km2, respectively). The region is bounded by the Indian Ocean in the south and the Himalayan mountain range in the north. The meteorological conditions over the South Asia region are controlled primarily by the movement of the inter-tropical convergence zone (ITCZ). When the ITCZ is located over the Indian Ocean (between Equator to 5oS) during boreal autumn, winter and spring, the region is generally dry without much occurrence of rainfall. When the ITCZ is located north of the region, about 70% of precipitations occur during the boreal summer (June-September). Some of these prevailing meteorological conditions are discussed in relations with CO2 and CH4 surface fluxes, and concentration variations in earlier studies (Patra et al. 2009, 2011a). We define the boundaries of the Southeast Asia region based on a geographically consistent unit formed by Brunei, Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, the Philippines, Timor-Leste, Papua New Guinea, Singapore, and Vietnam (Figure 1). The total area of the region is about 4.5×106 km2 of land with a population of 550×106 people in 2012 (World Bank 2012).

Figure 1: Landmass selected for the RECCAP South and Southeast Asia regions following the definition of the United Nations and by accounting for the similarities in vegetation types.

2.1. Emissions from the combustion of fossil fuels and cement production: Carbon dioxide emission statistics were taken from the CDIAC database of consumption of fossil fuels and cement production (Boden et al., 2011). CO2 emissions were derived from energy statistics published by the United Nations (2010) and processed according to methods described in Marland and Rotty (1984). CO2 emissions from the production of cement were based on data from the U.S. Department of Interior's Geological Survey (USGS 2010), and emissions from gas flaring were derived from data provided by the U.N., U.S. Department of Energy's Energy Information Administration (1994). 2.2. Emissions from land use and land use change: Emissions from land use change include the net flux of carbon between the terrestrial biosphere and the atmosphere resulting from deliberate changes in land cover and land use (Houghton, 2003). Flux estimates are based on a book-keeping model that tracks living and dead carbon stocks including wood products for each hectare of land cultivated, harvested Final Report: ARCP2013-01CMY-Patra/Canadell

7

or reforested. Data on land use change was from the Global Forest Resource Assessment of the Food and Agriculture Organization (FAO 2010). We also extracted information from national communication reports to the United Nations Framework Convention on Climate Change. 2.3. Fire Emissions. Fire emissions for the region were obtained from the Global Fire Emissions Database version 3.1 (GFEDv3.1). GFED is based on a combination of satellite information on fire activity and vegetation productivity (van der Werf et al. 2006; 2010). The former is based on burned area, active fires, and fAPAR from various satellite sensors, and the latter is estimated with the satellite-driven Carnegie Ames Stanford Approach (CASA) model. 2.4. Transport of riverine carbon. To estimate the land to ocean carbon flux we used the six ocean coastline segments with their corresponding river catchments for South Asia as described by the COSCAT database (Meybeck et al. 2006). The lateral transport of carbon to the coast was estimated at the river basin scale using the Global Nutrient Export from WaterSheds (NEWS) model framework (Mayorga et al. 2010), including NEWS basin areas. The carbon species models are hybrid empirically and conceptually based models that include single and multiple linear regressions developed by the NEWS effort and Hartmann et al. (2009), and single-regression relationships assembled from the literature. Modelled dissolved and particulate organic carbon (DOC and POC) loads used here (from Mayorga et al., 2010) were generated largely using drivers corresponding to the year 2000, including observed hydro-climatological forcings, though some parameters and the observed loads are based on data spanning the previous two decade. Total suspended sediment (TSS) exports were also estimated by NEWS. Dissolved inorganic carbon (DIC) estimates correspond to weathering-derived bicarbonate exports and do not include CO2 supersaturation; the statistical relationships developed by Hartmann et al. (2009) were adjusted in highly weathered tropical soils (ferralsols) to 25% of the modelled values found in Hartmann et al. (2009) to account for overestimates relative to observed river exports (J. Hartmann and N. Moosdorf, unpublished); adjusted grid-cell scale exports were aggregated to the basin scale using NEWS basin definitions (Mayorga et al. 2010), then reduced by applying a NEWS-based, basin-scale consumptive water removal factor from irrigation withdrawals (Mayorga et al. 2010). DIC modeled estimates represent approximately 19702000. Overall, carbon loads may be characterized as representing general conditions for the period 1980-2000. Carbon, sediment and water exports were aggregated from the river basin scale to corresponding COSCAT regions. 2.5. Fluxes by terrestrial ecosystem models. We use the net primary productivity (NPP) and heterotrophic respiration (RH) simulated by ten ecosystem models: HyLand, Lund-PotsdamJena DGVM (LPJ), ORCHIDEE, Sheffield–DGVM, TRIFFID, LPJ_GUESS, NCAR_CLM4C, NCAR_CLM4CN, OCN and VEGAS. The models used the protocol as described by the carbon cycle model intercomparison project (TRENDY) (Sitch et al. 2012; Piao et al. 2012; http://dgvm.ceh.ac.uk/system/files/Trendy_protocol%20_Nov2011_0.pdf), where each model was run from its pre-industrial equilibrium (assumed at the beginning of the 1900s) to 2009. We present net ecosystem productivity (NEP = NPP - RH) from two simulation cases; S1: where models consider change in climate and rising atmospheric CO2 concentration, and S2: where models consider change in atmospheric CO2 concentration alone. The historical changes in atmospheric CO2 concentration for the period of 1901-2009 were derived from ice core records and NOAA atmospheric observations (Keeling and Whorf, 8

Final Report: ARCP2013-01CMY-Patra/Canadell

2005). For the climate forcing datasets, monthly climate data for the period of 1901-2009 from CRU-NCEP datasets with a spatial resolution 0.5°×0.5° (http://dods.extra.cea.fr/data/p529viov/cruncep/) were used. Information on land use change was derived from HYDE 3.1 land cover dataset (Goldewijk, 2001, ftp://ftp.mnp.nl/hyde/hyde31_final/). These models do not include lateral fluxes of C exported away from ecosystems (from soils to rivers, biomass harvested products) nor fluxes resulting from forest and agricultural management. We performed correlation analyses between detrended net carbon flux and two climate drivers, annual temperature and annual precipitation, in order to diagnose the modelled interannual response of net carbon fluxes to these drivers (positive for carbon source, negative for carbon sink). The detrended fluxes were calculated by removing the 30-year linear trend of each variable (net carbon flux, annual temperature and annual precipitation), in order to avoid the confounding effects of the simultaneous trends of temperature or rainfall, with those of other environmental drivers such as rising CO2. 2.6. Atmospheric inverse models. The biospheric (non-fossil CO2) CO2 fluxes are available from state-of-the art atmospheric inversion models from the TransCom database at IPSL/LSCE (http://transcom.lsce.ipsl.fr; Peylin et al., 2013). Estimated fluxes from the following models are included in this analysis: C13_CCAM, C13_CCAM, Carbontracker_EU, Jena_s96_v3.2, JMA_2010, LSCE_an_v2.1, LSCE_var_v1.0, NICAM_MRI, RIGC_TDI64, TransCom-L3_mean. We also obtained regional specific inversion results for South Asia using the CARIBIC (Schuck et al., 2010) data in the upper troposphere over India and Pakistan, which is subsequently validated using the CONTRAIL (Machida et al., 2008) data of vertical profiles over Delhi and upper troposphere over Asia (Patra et al., 2011a). CONTRAIL observations are also used for inversion, with CARIBIC data for validation (Niwa et al., 2012). Measurements of atmospheric CO2 in the South Asia region are limited to Cabo de Rama, India for the period of 1993- 2002 (Bhattacharya et al., 2009). This site constrains the CO2 fluxes from India during winter to spring seasons only. Thus the use of aircraft measurements is indispensible for top-down flux estimates over the full seasonal cycle. 2.7. Methane fluxes Top-down estimates: Global distributions of CH4 emissions are prepared using site scale field measurements, inventories (in the case of fossil CH4 emissions and livestock emissions) and their extrapolation using remote sensing of wetland distribution and terrestrial ecosystem models (e.g., Mathews and Fung, 1987; Olivier and Berdowski, 2001; Ito and Inatomi, 2012). Components of these bottom-up estimations are scaled and used as an input to chemistry-transport models and compared with atmospheric mixing ratio measurements, or are used as prior flux estimates for inverse modeling of surface CH4 fluxes ((Patra et al., 2011a; Bousquet at al., 2006 and references therein). Patra et al. (2011b) prepared 6 distinct CH4 budgets; 5 of those being anthropogenic sources (EDGAR, 2010; version 3.2, 4.0) in combination with natural sources due to wetlands (Ringeval et al., 2010; Ito and Inatomi, 2012), biomass burning (van der Werf et al., 2006), and those from Fung et al. (1991), and one being based on inversion of atmospheric concentrations (Bousquet et al., 2006). Bottom-up estimates for India: Methane fluxes for India were estimated using bottom-up inventory data which relied on SPOT Vegetation NDVI, Radarsat Scan SAR (SN2) and IRS AWiFS to map the different rice lands and generate the feed/fodder area for livestock consumption (Manjunath et al. 2011; Chhabra et al. 2009). Final Report: ARCP2013-01CMY-Patra/Canadell

9

3. Results & Discussion 3.1. Emissions from fossil fuels and cement production 3.1.1 South Asia Table 1 shows the fossil fuel and cement CO2 emissions trends over the South Asia region and member countries over the past two decades. Growth rates are calculated as the slope of a fitted linear function, normalized by the average emissions for the period of interest. The average regional total emissions are estimated to be 278 and 444 TgC yr-1 for the periods of 1990s and 2000s, respectively. The regional total emissions have steadily increased from 213 TgC yr-1 in 1990 to 573 TgC yr-1 in 2009. About 90% of emissions from South Asia are due to fossil fuel consumptions in India at a normalized growth rate of 4.7% yr-1 for the period of 1990-2009. The decadal growth rates do not show significant differences between the 1990s (5.5% yr-1) and 2000s (5.3% yr-1) for the whole region, while an increased rate of consumptions was observed after 2005 (6.8% yr-1). This acceleration (Table 1) in fossil fuel consumption is largely due to the growth of the Indian economy, where the gross domestic product (GDP) doubled, from 34 trillions of Indian Rupees in 2005 to 67 trillions of Indian Rupees in 2010 (http://en.wikipedia.org/wiki/Economy_of_India).

Table 1. Average fossil fuel CO2 emissions and annual growth rates (%) for the decade of 1990s, 2000s, and the full RECCAP period (1990-2009). Country/ Region

Average Growth Emissions rate (TgC yr-1) (% yr-1) 199019902009 2009

Average Growth Emissions Rate (TgC yr-1) (% yr-1) 199019901999 1999

Average Growth Emissions Rate -1 (TgC yr ) (% yr-1) 200020002009 2009

Bangladesh 8.207

6.2

5.638

6.0

10.775

6.1

Bhutan

0.113

7.8

0.072

11.4

0.154

8.5

India

319.81

4.7

247.44

5.6

392.18

5.3

Nepal

0.702

5.6

0.514

13.8

0.889

2.3

Pakistan

29.986

4.1

23.019

4.7

36.932

5.7

Sri Lanka

2.368

5.8

1.629

8.9

3.107

2.2

South Asia

361

5.7

278

8.4

444

5.0

10

Final Report: ARCP2013-01CMY-Patra/Canadell

3.1.2. Southeast Asia For the period of 1990-2009, Southeast Asia had cumulative emissions from the combustion of fossil fuel, gas flaring and cement production of 4,450 TgC, at an average of 174 TgC yr-1 during 1990-1999 and 271 TgC yr-1 during 2000-2009. The top three cumulative emitters during the 20-year period were Indonesia (1535 TgC), Thailand (1137 TgC), and Malaysia (725 TgC), accounting for 34%, 26% and 16% of the total regional emissions, respectively (Figure 2).

Figure 2: Carbon emissions from the combustion of fossil fuels from countries in Southeast Asia

3.2. Emissions from Land-use change (LUC) 3.2.1. South Asia The annual net flux of carbon from land-use change in South Asia was a small sink (-11 TgC yr-1 for the 1990s and -14 TgC yr-1 for the period 2000-2009). The average sink over the 20year period 1990-2009 was -12.5 TgC yr-1. Three activities drove this net sink: establishment of tree plantations (-13 TgC yr-1 in the most recent decade), wood harvest (-3 TgC yr-1), and the expansion of croplands (2 TgC yr-1). Wood harvest results in a net sink of carbon because both industrial wood and fuelwood harvesting have declined recently, while the forest ecosystem productivity remained constant. Tree plantations (eucalyptus, acacia, rubber, teak, and pine) expanded by 0.2×106 ha yr-1 in the 1990-1999 period and by 0.3×106 ha yr-1 during 2000-2009 in the region (FRA, 2010).

Final Report: ARCP2013-01CMY-Patra/Canadell

11

Uptake of carbon as a result of these new plantations, as well as those planted before 1990, averaged -11 and -13 TgC yr-1 in the two decades, respectively. Industrial and fuelwood harvest (including the emissions from wood products and the sink in regrowing forests) was a net sink of -6 and -3 TgC yr-1 in the two decades, most of this sink from fuelwood harvest. The net sink attributable to logging suggests that rates of wood harvest have declined in recent decades, while the recovery of forests harvested in previous years drives a net sink in forests. The carbon sink in expanding plantations and growth of logged forests was offset only partially by the C source from the expansion of croplands, which is estimated to have released 6 TgC yr-1 and 2 TgC yr-1 during the 1990s and the first decade of 2000, respectively. The net change in forest area in South Asia was zero for the decade 1990-1999 and averaged 200,000 ha yr-1 during 2000-2009 (FRA, 2010). Given the rates of plantation expansion during these decades (200,000 ha yr-1 in the 1990-1999 period and by 300,000 ha yr-1 during 2000-2009), native forests were lost at rates of 200,000 ha yr-1 and 100,000 ha yr-1 in the two decades. The large changes in forest area, both deforestation and afforestation, lead to gross emissions (~120 TgC yr-1) and a gross uptake (~135 TgC yr-1) that are large relative to the net flux of 14 TgC yr-1. Thus, the uncertainty is greater than the net flux itself. The uncertainty is estimated to be 50 TgC yr-1, a value is somewhat less than 50% of the gross fluxes. The net flux for South Asia was determined to a large extent by land-use change (the expansion of tree plantations) in India, which accounts for 72% of the land area of South Asia, 85% of the forest area, and >95% of the annual increase in plantations. Although 11 estimates of the net carbon flux due to land use change for India published since 1980 have varied from a net source of 42.5 TgC yr-1 to a net sink of -5.0 TgC yr-1. The recent estimates by Kaul et al. (2009) for the late 1990s and up to 2009 suggest a declining source/increasing sink, consistent with the findings reported here for all of South Asia. Because India represents the largest contribution to land-use change in South Asia, and because there have been a number of analyses carried out for India, the discussion below focuses on India. A major theme of carbon budgets for India’s forests has been the roles of tree plantations versus native forests. The 2009 Forest Survey of India (FSI) reported a 5% increase in India’s forest area over the previous decade. This is a net change, however, masking the loss rate of native forests (0.8% to 3.5% per year) and a large increase in plantations (eucalyptus, acacia, rubber, teak, or pine trees) (~5700 km2 to ~18,000 km2 per year) (Puyravaud et al., 2010). The same theme is evident in the earlier carbon budgets for India’s forests. Ravindranath and Hall (1994) noted that, nationally, forest area declined slightly (0.04%, or 23,750 ha annually) between 1982 and 1990. At the state level, however, adding up only those states that had lost forests (still an underestimate of gross deforestation), the loss of forest area was 497,800 ha yr-1 between 1982 and 1986, and 266,700 ha yr-1 between 1986 and 1988. These losses were obviously offset by ‘gross’ increases in forest area in other states. Similarly, Chhabra et al. (2002) found a net decrease ~0.6 Mha in total forest cover for India 1988-1994, while district-level changes indicated a gross increase of 1.07 Mha and a gross 12

Final Report: ARCP2013-01CMY-Patra/Canadell

decrease of 1.65 Mha. These changes in area translated into a decrease of 77.8 TgC in districts losing forests and an increase of 81 TgC in districts gaining forests (plantations) during the same period. It seems odd, though not impossible, that carbon accumulated during this period while forest area declined. Clearly, the uncertainties are high. This analysis did not include shifting cultivation in South Asia, but Lele and Joshi (2008) attributed much of the deforestation in northeast India to shifting cultivation. Houghton (2007) also omitted the conversion of forests to waste lands, while Kaul et al. (2009) attribute the largest fluxes of carbon to conversion of forests to croplands and wastelands. It seems unlikely that forests are deliberately converted to wastelands. Rather, wastelands probably result either from degradation of croplands (which are then replaced with new deforestation) or from over-harvesting of wood. Fuelwood harvest, and its associated degradation of carbon stocks, and even deforestation, seems another primary driver of carbon emissions in South Asia. For example, Tahir et al. (2010) report that the use of fuelwood in brick kilns contributed to deforestation in Pakistan, where 14.7% of the forest cover was lost between 1990 and 2005. In Nepal, Upadhyay et al. (2005) attribute the loss of carbon through land-use change to fuelwood consumption and soil erosion, and Awasthi et al. (2003) suggest that fuelwood harvest at high elevations of Himalayan India may not be sustainable. On the other hand, Unni et al. (2000) found that fuelwood harvest within a 100-km radius of two cities showed both conversion of natural ecosystems to managed ones and the reverse, with no obvious net reduction in biomass. They inferred that the demand for fuelwood on forest and nonforest biomass was not great enough to degrade biomass. The net sink estimated for South Asia in this study may have underestimated the emissions from forest degradation; logged forests were assumed to recover unless they were converted to another use. If wood removals exceed the rate of wood growth, carbon stocks will decline (forest degradation) and may ultimately be lost entirely (deforestation). 3.2.2. Southeast Asia Of the 11 countries included in Southeast Asia, Indonesia has the largest area of forest in 2010 (97,857,000 ha), approximately 3x greater than the country with the next largest forest (Myanmar and Papua New Guinea) (FAO, 2010). Indonesia also has the highest rate of deforestation of native forests (3,275,000 ha yr-1) (2000-2009) (FAO,2010), with Myanmar next (1,687,000 ha yr-1), followed by Papua New Guinea (705,000 ha/yr), Malaysia (668,000 ha yr-1), and Cambodia (632,000 ha yr-1). Thailand, Indonesia, and Vietnam have the largest areas of tree plantations in 2010 (3,986,000, 3,549,000, and 3,512,000 ha, respectively) (FAO, 2010). The net flux of carbon from land-use change in Southeast Asia averaged 341 TgC yr-1 and 194 TgC yr-1 for the decades of 1990-1999 and 2000-2009, respectively (268 TgC yr-1 averaged over the period 1990-2009). The decline in emissions over the two decades is consistent with the reduced rates of deforestation reported by FAO (FAO, 2010) and Hansen et al. (2009). Nevertheless, high rates of deforestation (as high as 5% annually) continue at present in at least two locations, the eastern lowlands of Sumatra and the peatlands of Sarawak, Borneo (Miettinen et al., 2011).

Final Report: ARCP2013-01CMY-Patra/Canadell

13

The types of land use most important in the carbon budget are the conversion of forests to croplands and to shifting cultivation, with smaller net releases from logging. The net flux from logging includes both the emissions from wood products and the accumulations of carbon in regrowing forests. The flux attributed to the expansion of croplands and shifting cultivation vary from decade to decade, and, because the approach used to assign deforestation to either croplands or shifting cultivation is subject to error, it is perhaps better to consider the two combined (i.e., 202, 276, and 184 TgC yr-1 for the agricultural expansion in the three decades, respectively). It is important to note that the net flux of carbon from deforestation for croplands does not include the large emissions of carbon from the draining and burning of peatland forests, which alone contributed an estimated 300 TgC yr-1 to the net flux from Southeast Asia (Hooijer et al., 2010). It would probably be double-counting to add this 300 TgC yr-1 release to the estimates reported here, because the cultivation of upland soils is estimated here to have released 115 TgC yr-1. A conservative estimate would be to add ~200 TgC yr-1 to the net flux reported here, yielding a net flux as high as 0.54 PgC yr-1 1990-1999 and ~0.4 PgC yr-1 for 2000-2009. Shifting cultivation is poorly documented at continental scales, but a recent analysis estimated the carbon emitted from fires associated with shifting cultivation in the tropics (Silva et al., 2011). For the countries of Southeast Asia (but lacking Papua New Guinea) their estimate was 48 TgC yr-1. It is lower than the gross emissions of 155 and 100 TgC yr-1 (for the 1990s and 2000-2009, respectively) estimated here for gross emissions of carbon from fires for shifting cultivation. Shifting cultivation is common in the region, and recent field studies have documented the recovery of forest in the fallows of shifting cultivation in Vietnam (Do et al., 2010) and in Sarawak, Malaysia (Jepsen, 2006). Recent attention has focused on Indonesia and Papua New Guinea. The loss of forests in Sumatra and Kalimantan (Borneo), in particular, has been going on at least since the 1980s (Curran et al., 2004). That loss has continued to the present (Hansen et al., 2009; Broich et al., 2011) and seems likely to continue (Fuller et al., 2011). Rates of forest loss are highest at present in peat swamp forests (Langner et al., 2007; Miettinen et al., 2011) that are being drained and planted with oil palm plantations. The draining and burning releases large quantities of carbon to the atmosphere (Hooijer et al., 2010) and removes an important carbon sink from the landscape (Dommain et al., 2011). Coffee has also been important in driving deforestation in southwest Sumatra (Gaveau et al., 2008). In Papua New Guinea population growth has led to deforestation for new agricultural lands (Ningal et al., 2007), but logging is also important. Bryan et al. (2009) estimate that 41% of the 53 TgC released from Papual New Guinea as a consequence of deforestation and degradation in 2001 resulted from logging. Selective logging damages and kills trees in addition to those harvested (Bryan et al., 2010), but in the absence of further disturbance, logged forests have the capacity to recover (Yosi et al., 2011). In addition to the reduction of biomass that follows logging, forest fragmentation may also contribute to carbon emissions. Oil palm and rubber plantations were major contributors to forest fragmentation in Malaysia (Abdullah and Nakagoshi, 2007), oil palm being more important in wetlands and rubber plantations more important in forests. 14

Final Report: ARCP2013-01CMY-Patra/Canadell

3.3 Emissions from fires 3.3.1. South Asia. South Asia is not a large source of CO2 emission due to biomass burning as per the GFED3.1 (Global Fire Emission Database, version 3.1; van der Werf et al., 2006; 2010). Out of about global total emissions of 2,013±384 TgC yr-1 due open fires as detected by the various satellites sensors, 47±30 TgC yr-1 (2.3% of the total) only are emitted in the South Asian countries. The average and 1σ standard deviations are calculated from the annual mean emissions in the period 1997-2009. The total emission is reduced to 44±13 TgC yr-1 if the period of 2000-2009 is considered. The total fire emissions can be attributed to agriculture waste burning (14±4 TgC yr-1), deforestation fires (21±11 TgC yr-1), forest fires (2.6±1.5 TgC yr-1), savanna burning (4.8±1.9 TgC yr-1) and woodland fires (1.8±1.0 TgC yr-1) for the period of 2000-2009. The seasonal variation of CO2 emissions due to fires is discussed in subsection 3.7. Fire emissions due to agricultural activities will be largely recovered through the annual cropping cycles, and emissions from wildfires in natural ecosystems will be also largely recovered through regrowth over multiple decades (unless there is a fire regime change for which we have no evidence). For these reasons, carbon emissions from fires from the GFED product will not be used to estimate the regional carbon budget, given that fire emissions associated with deforestation are already included in the land use change flux presented in this study. GFED fire fluxes are used to interpreted interannual variability. 3.3.2

Southeast Asia

Total carbon fire emissions (from CO2) from Southeast Asia were 227 TgC yr-1 for the 19972011 period. The amount is comparable to 254 TgC yr-1 from fossil fuel carbon emissions during 2000-2008 although their long-term net contribution to radiative forcing in the atmosphere is different. Fire emissions are a gross flux that can partially be offset by vegetation regrowth following fire while fossil fuel emissions are a net flux to the atmosphere. Of the total fire emissions for the period studied, emissions from deforestation and degradation fires are the single most dominant source accounting for 51%. Uniquely to Southeast Asia, peat fires are an important source of emissions as fires can last for months after the fires begin. They account for 33% of the total emissions, followed in decreasing order by fire emissions from savannas, forests, agriculture and woodlands. Fire emissions are large contributors to inter-annual variability with larger fluxes during The Southern Oscillation/El Nino periods. The El Niño of 1997-1998 stands out from other periods with emissions due to peat fires seven times higher than the long-term mean (Figure 3). These results are consistent with bottom-up estimates based on field measurements (Page et al., 2002) and inverse modelling (Patra et al., 2005). The lower inter-annual variability in carbon emissions due to deforestation fires, compared to peat fires, could indicate greater control of natural climate variability on the extent of peat burning. It is well established, however, that all fires, small and big, are caused by human activity and that there I a close coupling between human activity and natural climate variability in determining fire emissions from the region ([Field and Shen, 2008].

Final Report: ARCP2013-01CMY-Patra/Canadell

15

Figure 3: Inter-annual variability and mean of difference fire emission components for the Southeast Asia region.

3.4 Riverine Carbon Flux 3.4. South Asia The total carbon export from South Asian rivers was 42.9 TgC yr-1, with COSCAT exports ranging from 0.01 to 33.4 TgC yr-1 for the period of 1980-2000. Considering that about 611 TgC yr-1 is estimated to be released from global river systems (Cole et al., 2007; Batin et al., 2009), rivers in the South Asia region contribute about 7% of global riverine carbon export, which is more than twice the world average rate (the South Asia has about 3% of the global land area). The largest riverine carbon export was observed from the Bengal Gulf COSCAT, which is dominated by the combined Ganges-Brahmaputra discharge. The riverine carbon exports from the other five remaining COSCAT basins were lower by up to two orders of magnitude, ranging from only 0.01 to 4.4 TgC yr-1. Because large riverine carbon loads can be due to large basin area, we also provide the basin carbon yield (riverine carbon load per unit area, excluding PIC). Basin carbon yields varied by a few orders of magnitude, ranging from 0.04 to 18.4 gC m-2 yr-1. The largest basin carbon yield was again from the Bengal Gulf COSCAT. However, Laccadive Basin COSCAT and West Deccan Coast COSCAT also released 9.5 and 8.2 gC m-2 yr-1, respectively. The global mean terrestrial carbon yield can be calculated by dividing the global riverine carbon export of 611 TgC yr-1 (Aufdenkampe et al. 2011, Battin et al. 2009) by the total land area of 149 million km2, providing a global mean yield of 4.1 gC m-2 yr-1. Therefore, the three COSCAT regions in South Asia released more carbon per unit area than the global average. Considering that riverine carbon export is heavily dependent on discharge, this is not surprising since the three COSCAT regions have annual discharge values 40 to 120% larger than the global average discharge to the oceans of 340 mm yr-1 (Mayorga et al., 2010).

16

Final Report: ARCP2013-01CMY-Patra/Canadell

The three COSCAT regions with the largest basin carbon yields (Bengal Gulf, Laccadive Basin, and West Deccan Coast) also corresponded to the area of highest NPP of the South Asia (Kucharik et al., 2000), consistent with areas of cultivated crops and forested regions (Figure 1). This suggests that terrestrial inputs of carbon to the river system of the region can be a significant factor next to the riverine discharge. The relative contribution of DIC (Dissolved Inorganic Carbon), DOC (Dissolved Organic Carbon), and POC (Particulate Organic Carbon) to the total riverine carbon exports varied depending on the region. The Bengal Gulf COSCAT exported riverine DIC, DOC, and POC of 9.3, 7.0, and 17.1 TgC yr-1, respectively, demonstrating the strong POC contribution. Riverine TSS (Total Suspended Sediment) loads and basin yields were also the largest from the Bengal Gulf COSCAT, indicating the strong correlation between POC and TSS. The carbon emitted by soils to rivers headstreams can be degassed to atmosphere as CO2 or deposited into sediment during the riverine transport from terrestrial ecosystem to oceanic ecosystem (Aufdenkampe et al., 2011, Cole et al., 2007). The estimated carbon release to the atmosphere from Indian (inner) estuaries (1.9 TgC yr-1; Sarma et al., 2012) is relatively small compared to the total river flux of South Asia region. The mosoonal discharge through these estuaries have a short residence time of OC, which helps the OC matters to be transported relatively unprocessed to the open/deeper ocean. The average residence time during the monsoonal discharge period is less than a day, as observed over the period of 1986-2010, with longest residence time of 7 days for the years of low discharge rate (Acharyya et al., 2012). On an average the processing rate of OC in estuaries is estimated to be 30% in the Ganga-Brahmaputra river system in Bangladesh, and the remaining 70% are stored in the deep water of Bay of Bengal (Galy et al., 2007).

3.4.2 Southeast Asia The total annual transport of carbon from land to the ocean through riverine transport is 119 TgC yr-1 in the form of DIC (60.6 TgC yr-1), DOC (26.5 TgC yr-1), and POC (32.3 TgC yr-1). The 12 COSCAT regions Southeast Asia have a total riverine carbon export of 119.2 TgC yr 1 , ranging from 0.6 to 26.3 TgC yr-1 for individual regions. Considering an estimate of 0.9 PgC yr-1 released from global river systems [Aufdenkampe et al., 2011; Battin et al., 2009], rivers in Southeast Asia contribute 13% of global riverine carbon export. The area of the region is 5.72 million km2, similar to the area of the neighboring South Asia (SA) (5.05 million km2) (Patra et al. 2012), but riverine carbon export from the Southeast Asia region is about three times larger than that of South Asia.

3.5. Modeled long-term mean ecosystem fluxes from biosphere models 3.5.1 South Asia Bottom-up estimates from all ten ecosystem models, forced by rising atmospheric CO2 concentration and changes in climate (S2 simulation), agree that terrestrial ecosystems of South Asia acted as a net carbon sink during 1980-2009. The average magnitude of the sink (NEP) estimated by the ten models is -210±164 TgC yr-1, ranging from -80 TgC yr-1 to -651 TgC yr-1. Rising atmospheric CO2 alone (S1 simulation) accounts for 89%-110% of the carbon sink estimated in the CO2+Climate simulations (S2), suggesting a dominant role of the CO2 fertilization effect in driving the regional sink. The decadal average NEPs are Final Report: ARCP2013-01CMY-Patra/Canadell

17

193±136, -217±174 and -220±186 TgC yr-1, respectively, for the 1980s, 1990s and 2000s. The net primary productivity (NPP) for the same decades are 2117±372, 2160±372 and 2213±358 TgC yr-1, respectively. Five of the eight models providing CO2+Climate simulations (S2) show that climate change alone led to a carbon source of 0.1 TgC yr-1 to 63 TgC yr-1 over the last three decades (the difference between simulation S2 and S1); the three other models (OCN, ORC and TRI) show that climate change enhanced the carbon sink by -14, -6 and -4 TgC yr-1. Such model discrepancies result in average net carbon flux driven by climate change is near neutral (10±22 TgC yr-1).

3.5.2. Southeast Asia The estimated average NPP from 10 models (CLM4C, CLM4CN, Hyland, LPJ, LPJ-GUESS, OCN, ORCHIDEE, SDGVM, TRIFFID, VEGAS) driven by climate change and rising atmospheric CO2 concentration (simulation S2) is 5.1±0.8 PgC yr-1 (615.4±95.6 gC m-2 yr-1) All models show a significant NPP increase at an average rate of 0.018±0.006 PgC yr-1 (0.3±0.1 % yr-1) over the last three decades. Such a significant increase in NPP is mainly the result of the atmospheric CO2 fertilization effect (0.015±0.005 Pg C yr-1). We found that all models agreed that climate change plays a smaller role in driving the NPP trend as calculated by the difference between model estimated NPP in simulation S2 and that in simulation S1 (rising atmospheric CO2 only). Temperature has a positive and significant trend with NPP (0.01°C yr-1, P=0.03) and precipitation has a positive but marginally significant trend with NPP (7 mm yr-2, P=0.06).

3.6. Modelled long-term mean ecosystem fluxes from inversions 3.6.1 South Asia Top-down estimates of land-atmosphere CO2 biospheric fluxes (i.e. without fossil fuel emissions, and inclusive of LUC flux and Riverine export) are estimated by using atmospheric CO2 concentrations and chemistry-transport models. Results are available from 11 atmospheric inverse models participating in the TransCom intercomparison project with varying time period between 1988–2008 (Peylin et al., 2012). The inversions were run without any observational data over the South Asia region for most part of the 2000s. Therefore, we place a very low confidence in the TransCom inversion results, and a medium confidence in the results of two additional regional inversions using aircraft measurements over the region. The estimated net land-atmosphere CO2 biospheric fluxes from the two regional inversions are -317 and -88.3 TgC yr-1 (Patra et al., 2011a; Niwa et al., 2012). The range of biospheric CO2 fluxes estimated by the 11 TransCom inversions is -158 to 507 TgC yr-1, with a median value being a sink of -35.4 TgC yr-1 with a 1-σ standard deviation 182 TgC yr-1. The median of the TransCom inversions is chosen for filtering the effect of outliers values. In summary, for this RECCAP carbon budget, we propose as a synthesis of the inversion approach the mean value of the two ‘best’ inversions using region-specific CO2 data and the median of TransCom models (-147±150 TgC yr-1). For comparison, the NBP is calculated as -104±150 TgC yr-1 (Top-down biospheric flux – Riverine export; further details of NBP calculation in section 3.10.1).

18

Final Report: ARCP2013-01CMY-Patra/Canadell

3.6.2 Southeast Asia The mean net biospheric CO2 flux (excluding fossil fuel emissions) is estimated at 165 ± 378 TgC yr-1 based on 10 inverse models for the period of 2000-2008. The median value of 174 TgC yr-1 is not distinctly different from the mean value, indicating all the inversion results are spread widely around the mean. To this flux we add carbon exported out of the region through routes other than the atmosphere to complete the carbon budget. Thus, we add the riverine carbon export of 119 TgC yr-1 to estimate a mean of 284±378 TgC yr-1 as the net biospheric production of Southeast Asia (or carbon exported away from terrestrial ecosystems).

3 2 1 0 -1 -2

12 10 8 6 3-monthly mean +/- 1-sigma

2000 -1

CO2 flux anomaly [TgC yr ]

ENSO Index

number of models

Flux anomalies are calculated by subtracting the long-term mean seasonal cycle from the original fluxes time series at monthly time intervals for each model (Figure 4). Although the net CO2 flux estimated by inverse modeling greatly dependent on the selection of forward transport model, the flux anomalies, however, can be determined at greater consistency between the transport models. Thus we restrict detailed discussion to flux anomalies. Strong correlation of CO2 flux anomalies is found with the ENSO Index at short to medium time scales, ranging from 3-months to several years (correlation coefficient = 0.57, 0.61 and 0.38 for the mean, mean+1σ and mean-1σ fluxes, respectively). Patra et al. (2005) have found correlation coefficients of 0.61, -0.63 and 0.41 for the ENSO Index, rainfall and temperature, respectively, at 2, 5 and 2 months time lag, with the CO2 flux anomalies in the period of 1994-2001

1500 1000 500 0 -500 -1000 -1500 1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Figure 4: Interannual variations in CO2 emission estimated by the inverse models (bottom panel) showing close link with the ENSO index (top panel) for the Southeast Asia.

Final Report: ARCP2013-01CMY-Patra/Canadell

19

3.7. Seasonal variability of CO2 fluxes Figure 5 shows the comparisons of carbon fluxes as estimated by the terrestrial ecosystem models (NEP), atmospheric-CO2 inverse models (NBP) and fire emissions as estimated from satellite products and modeling. According to the ecosystem and inversion models, the peak carbon release is around April-May, while the peak of CO2 uptake is between July and October. The dynamics as seen by the TransCom (global) inversion models is quite unconstrained due to the lack of atmospheric measurements in the region. A recent study (Patra et al., 2011a) shows that the peak CO2 uptake rather occurs in the month of August when inversion is constrained by regional measurements from commercial aircrafts. The months of peak carbon uptake are consistent with regional climate seasonality, i.e., the observed maximum rainfall during June-August months. This predominantly tropical biosphere is likely to be limited by water availability as the average daytime temperatures over this region are always above 20oC and rainfall is very seasonal. The peak-to-trough seasonal cycle amplitudes of NEP simulated by the ecosystem models are of similar magnitude (~3000 TgC yr-1) compared to those estimated by one of the inversion constrained by aircraft data (Patra et al., 2011a). The other regional inversion using atmospheric observations within the region estimated a seasonal cycle amplitude about 50% greater, mainly due to large CO2 release in the months of May and June (Niwa et al., 2012). A denser observational data network and field studies are required for narrowing the gaps between different source/sink estimations.

a. Inverse models

3000 1500 0

TransCom (Peylin et al., 2012) Patra et al. (2011) Niwa et al. (2012)

-1500

3000

CLM4CN HYL LPJ_GUESS LPJ OCN ORC SDGVM TRI VEGAS CLM4C

1500

-1

CO2 flux (TgC yr ); South Asia

-3000

0 -1500 -3000

b. Ecosystem models

600 c. Fire emissions 400

200

0 J F MAM J J A S O N D J F MAM J J A S O N D 2007 2008

Figure 5: Seasonal cycles of South Asian fluxes (TgC yr-1) as simulated by atmospheric inversions (a. top panel), terrestrial ecosystem models (b. middle panel) and fire emissions modelling (c. bottom panel).

20

Final Report: ARCP2013-01CMY-Patra/Canadell

3.8. Inter-annual variability of carbon fluxes Because aircraft CO2 observations over South Asia region are limited to only two years (2007 and 2008), we will exclude inverse model estimates from the discussions on interannual variability. All ten terrestrial ecosystem models agree that there is no significant trend in the net carbon flux (positive values mean carbon source, negative values mean carbon sink) over South Asia from 1980 to 2009 (Figure 6). The estimated net carbon flux (simulation scenario S2) over South Asia exhibits relatively large year-to-year change among the two simulation scenarios. The interannual variation of the 30-year net carbon flux estimated by the average of the ten models is 63 TgC yr-1 measured by standard deviation, or 30% measured by coefficient of variation (CV). In fact, the CV of the 30-year net carbon flux estimated by different models show a large range from 14% to 166%, and only two models show a CV of larger than 100%. The model ensemble unanimously show that interannual variations in simulated net carbon flux is driven by the interannual variability in gross primary productivity (GPP) rather than that in terrestrial heterotrophic respiration (HR), suggesting that variations in vegetation productivity play a key role in regulating variations of the net carbon flux. Similar results were also found in other regions such as Africa (Ciais et al., 2009). To study the effect of climate change on net carbon flux variations, we performed correlation analyses between detrended anomalies of the modelled net carbon flux and detrended anomalies of climate (annual temperature and annual precipitation) over the last three decades (see Methods section). All models predict that carbon uptake decrease or reversed into net carbon source responding to increasing temperature, with two models (LPJ_GUESS and VEGAS) showing this positive correlation between temperature and net carbon flux statistically significant (r>0.4, P